Dropout detection in continuous analyte monitoring data during data excursions

ABSTRACT

Methods, devices, and systems are provided for identifying dropouts in analyte monitoring system sensor data including segmenting sensor data into a plurality of time series wherein each time series is associated with a different instance of a repeating event, selecting a first time series to analyze for dropouts from the plurality of time series; comparing the selected first time series to a second time series among the plurality of time series, determining whether the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, and displaying, on a computer system display, an indication that the selected first time series includes a dropout if the selected first time series includes a portion that is more than the predefined threshold lower than the corresponding portion of the second time series.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/193,881 filed Nov. 16, 2018, now U.S. Pat. No. 10,345,291, which is acontinuation of U.S. patent application Ser. No. 14/424,026 filed Feb.25, 2015, now U.S. Pat. No. 10,132,793, which claims priority to PCTApplication No. PCT/US2013/055883 filed Aug. 20, 2013, which claimspriority to U.S. Provisional Patent Application No. 61/695,164, filed onAug. 30, 2012, entitled “Dropout Detection in Continuous AnalyteMonitoring Data During Data Excursions”, the disclosures of each ofwhich are incorporated herein by reference in their entirety for allpurposes.

BACKGROUND

The detection of the concentration level of glucose or other analytes incertain individuals may be vitally important to their health. Forexample, the monitoring of glucose levels is particularly important toindividuals with diabetes or pre-diabetes. People with diabetes may needto monitor their glucose levels to determine when medication (e.g.,insulin) is needed to reduce their glucose levels or when additionalglucose is needed.

Devices have been developed for automated in vivo monitoring of analytetime series characteristics, such as glucose levels, in bodily fluidssuch as in the blood stream or in interstitial fluid. Some of theseanalyte level measuring devices are configured so that at least aportion of the devices are positioned below a skin surface of a user,e.g., in a blood vessel or in the subcutaneous tissue of a user. As usedherein, the term analyte monitoring system is used to refer to any typeof in vivo monitoring system that uses a sensor disposed with at least aportion subcutaneously to measure and store sensor data representativeof analyte concentration levels automatically over time. Analytemonitoring systems include both (1) systems such as continuous glucosemonitors (CGMs) which transmit sensor data continuously or at regulartime intervals (e.g., once per minute) to a processor/display unit and(2) systems that transfer stored sensor data in one or more batches inresponse to a request from a processor/display unit (e.g., based on anactivation action and/or proximity using a near field communicationsprotocol).

In some cases, analyte monitoring systems have been found tooccasionally provide false low readings for relatively short periods(e.g., non-zero-mean signal artifacts). These false low readings,referred to as “dropouts,” are distinct from a situation where noreading at all is provided. When no data at all is provided, an analytemonitoring system can easily detect that there is a problem becausethere simply is no signal from the sensor. In the case of a dropouthowever, there is still a signal and the data may appear to be correctbut in fact, the data is temporarily incorrect. In a CGM for example,such false data can trigger an unnecessary low blood sugar (e.g.,hypoglycemia event) false alarm. Thus, what is needed are systems,methods, and apparatus to reliably determine when a dropout has occurredin analyte monitoring system sensor data.

SUMMARY

The present invention provides systems, methods, and apparatus thatallow a user to analyze a collection of analyte monitoring system sensordata to identify dropouts. By improving dropout identificationcapabilities, the present invention enables researchers to betterunderstand dropout characteristics and thereby potentially develop adetection and correction algorithm that, for example, could beincorporated into a future analyte monitor system to detect and correctfor dropouts as they are happening. In addition, improved dropoutidentification can facilitate research efforts to mitigate these errorsand to provide a basis to compare sensor designs. Better dropoutrecognition may also allow healthcare providers and patients tocalibrate analyte monitor system alarm thresholds more accurately toreduce false alarms. Embodiments of the present invention segmentsanalyte monitoring system sensor data into time series associated withrepeating events, such as meals, that cause analyte levels to vary overtime. Using one or more of curve smoothing, time dilation, and dynamicrange scaling techniques to normalize the data for comparison, two ormore time series that correspond to the same repeating event (e.g.,breakfast) on different days are compared. If a time series includes aperiod with an anomalous low level compared with the same period inanother corresponding time series, the period is identified as adropout.

Some embodiments of the present disclosure include computer-implementedmethods of identifying a dropout in analyte monitoring system sensordata. The methods include receiving a signal representative of sensordata from an analyte monitoring system related to an analyte level of apatient measured over time; storing the sensor data in a computer systemstorage device; segmenting the sensor data into a plurality of timeseries wherein each time series is associated with a different instanceof a repeating event; selecting a first time series to analyze fordropouts from the plurality of time series; comparing the selected firsttime series to a second time series among the plurality of time series;determining whether the selected first time series includes a portionthat is more than a predefined threshold lower than a correspondingportion of the second time series; and displaying, on a computer systemdisplay, an indication that the selected first time series includes adropout if the selected first time series includes a portion that ismore than the predefined threshold lower than the corresponding portionof the second time series. The invention also includes a computer systemand a computer program product for identifying a dropout in analytemonitoring system sensor data. Numerous other aspects and embodimentsare provided. Other features and aspects of the present invention willbecome more fully apparent from the following detailed description, theappended claims, and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example graph of analyte monitoring system sensor dataplotted over time in accordance with some embodiments of the presentinvention.

FIG. 2 depicts an example graph of segmented and overlaid analytemonitoring system sensor data plots in accordance with some embodimentsof the present invention.

FIGS. 3A to 3D depict an example graph sequence of two smoothed timeseries plots of sensor data corresponding to the same repeating eventoccurring on different days, one having been time dilated and dynamicrange adjusted, in accordance with some embodiments of the presentinvention.

FIGS. 4A to 4C depict an example graph sequence of two smoothed timeseries plots of sensor data, one having been time dilated in accordancewith some embodiments of the present invention.

FIG. 5 depicts a flow chart illustrating an example method in accordancewith some embodiments of the present invention.

DETAILED DESCRIPTION

The present invention provides systems, methods, and apparatus toidentify dropouts in sensor data from an analyte monitoring system, suchas, for example, any type of in vivo monitoring system that uses asensor disposed with at least a portion subcutaneously to measure andstore sensor data representative of analyte concentration levelsautomatically over time. Analyte monitoring systems may include CGMswhich are programmed to transmit sensor data according to apredetermined transmission schedule, continuously, or at regular timeintervals to a processor/display unit and systems that transfer storedsensor data in one or more batches in response to a request from aprocessor/display unit, i.e., not according to a predeterminedtransmission schedule. Without requiring a patient to wear two or moreanalyte sensors, the present invention is operable to identify dropoutsin data from a single analyte sensor. According to some embodiments ofthe present invention, data representative of a patient's monitoredanalyte concentration level (herein referred to as “sensor data”)previously captured over a period of time (e.g., two weeks) is segmentedinto time series representative of sensor data reflecting the effects ofa meal or other repeating event that causes data “excursions” from astable analyte concentration level. Several time series representingrepeating events, for example, a meal, or an activity occurring at afixed time of day, may be identified for comparative analysis. In someembodiments, event time markers, either manually input into the systemor automatically determined from meal start estimates (without userintervention), may be used to identify the beginning and end of a timeseries. Once two or more time series have been identified ascorresponding to different instances of the same repeating event, thecorresponding time series can be analyzed to detect dropouts. In someembodiments of the present invention, corresponding time series arecompared to identify low analyte level differences that representdropouts. However, since a patient's analyte levels do not follow anidentical pattern each day and since excursion events such as meals arelonger or shorter on different days, data modulation algorithms ortechniques may be employed to normalize data (time series normalizeddata) or otherwise process or transform data to match it to other timeseries data for comparison. Harmonizing sets of un-harmonized sensordata time series to each other enables similar data sets that are notexactly identical to be compared to each other. Time series modulationalgorithms or techniques may employ one or more of time series curvesmoothing, dynamic range matching, and time dilation, to aid in thecomparison of the corresponding time series.

The invention may be applied to any analyte concentration leveldetermination system that may exhibit or at least be suspected ofexhibiting, or that may be susceptible to, dropouts. Embodiments of theinvention are described primarily with respect to continuous glucosemonitoring devices and systems but the present invention may be appliedto other analytes and analyte characteristics, as well as data frommeasurement systems that transmit sensor data from a sensor unit toanother unit such as a processing or display unit in response to arequest from the other unit. For example, other analytes that may bemonitored include, but are not limited to, acetyl choline, amylase,bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g.,CK-MB), creatine, DNA, fructosamine, glutamine, growth hormones,hormones, ketones, lactate, peroxide, prostate-specific antigen,prothrombin, RNA, thyroid stimulating hormone, and troponin. Theconcentration of drugs, such as, for example, antibiotics (e.g.,gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs ofabuse, theophylline, and warfarin, may also be monitored. In thoseembodiments that monitor more than one analyte, the analytes may bemonitored at the same or different times. The present invention alsoprovides numerous additional embodiments.

Embodiments of the present invention may include a programmed computersystem adapted to receive and store data from an analyte monitoringsystem. The computer system may include one or more processors forexecuting instructions or programs that implement the methods describedherein. The computer system may include memory and persistent storagedevices to store and manipulate the instructions and sensor datareceived from the analyte monitoring system. The computer system mayalso include communications facilities (e.g., wireless and/or wired) toenable transfer of the sensor data from the analyte monitoring system tothe computer. The computer system may include a display and/or outputdevices for identifying dropouts in the sensor data to a user. Thecomputer system may include input devices and various other components(e.g., power supply, operating system, clock, etc.) that are typicallyfound in a conventional computer system. In some embodiments, thecomputer system may be integral to the analyte monitoring system. Forexample, the computer system may be embodied as a handheld or portablereceiver unit within the analyte monitoring system.

The various methods described herein for performing one or moreprocesses also described herein may be embodied as computer programs(e.g., computer executable instructions and data structures) developedusing an object oriented programming language that allows the modelingof complex systems with modular objects to create abstractions that arerepresentative of real world, physical objects and theirinterrelationships. However, any practicable programming language and/ortechniques may be used. The software for performing the inventiveprocesses, which may be stored in a memory or storage device of thecomputer system described herein, may be developed by a person ofordinary skill in the art based upon the present disclosure and mayinclude one or more computer program products. The computer programproducts may be stored on a computer readable medium such as a servermemory, a computer network, the Internet, and/or a computer storagedevice.

Turning to FIG. 1, a graph 100 of glucose concentration level (mg/dL)over time (days) depicting an example of sensor data generated by ananalyte monitoring system is shown. The graph 100 plots a patient'sglucose level over the lifetime of the analyte monitoring system'ssensor. For example, the particular example graph 100 shown representsdata collected by an analyte monitoring system over five days. Thevariations in the patient's glucose level are referred to as excursionsand the excursions may be associated with events in the patient's dailyactivities. In particular, meals cause excursions as the in-take of foodaffects the patient's glucose level. However, looking at the graph 100in FIG. 1, it may not be easy to identify meal excursions because anyeating patterns of the patient are not obvious when the data is plottedas one long sequence.

According to embodiments of the present invention, the sensor data maybe segmented by the computer system into daily plots that are overlaidto reveal the patient's behavior patterns as shown in the graph 200 ofFIG. 2. The overlaid plots may thus be used to identify meal excursionsin the data. For example, in the particular graph 200 shown, thegrouping of peaks at about 2 PM and about 8 PM suggest that these peakscorrespond to daily meals consumed at roughly the same time each daywherein the 2 PM peaks correspond to lunch and the 8 PM peaks correspondto dinner.

In some embodiments, the plots of the sensor data may be, for example,sliding-average smoothed to remove “noise” and to facilitate easiercomparison of the plots. In an unweighted sliding-average smoothing,each data point of the plot is replaced with the average of “m” adjacentpoints, where “m” is a positive integer referred to as the “smoothingwidth” or “smoothing window” in the case of a data plot over time ashere. In some embodiments, various different smoothing windows (e.g., 15minutes, 20 minutes, 30 minutes, etc.) may be tried by the computersystem to find acceptable smoothening results while not significantlydistorting features associated with true glucose excursions. Forexample, the smallest smoothing window that removes most of the jaggedpoints on the curve without changing the overall shape of the curve maybe selected. In some embodiments, other methods of smoothing may beused.

Once the overlaid daily plots are smoothed, they may be segmented intotime series that correspond to repeating events (e.g., meals). A windowof time may be defined where two or more time series of sensor data(e.g., x1, x2, x3, . . . , xN) from the same patient is to be compared.For example, the start of the time window for a repeating event thatoccurred on the last day of the sensor data may be determined using thelatest meal marker (e.g., breakfast, lunch, dinner). This meal markermay be input by the user or determined by a meal start estimator.Details describing obtaining a meal marker from a meal start estimatormay be found in U.S. patent application Ser. No. 61/582,209, filed Dec.30, 2011, entitled “Method and Apparatus for Determining Medication DoseInformation,” which is hereby incorporated by reference. The end of thetime window for a repeating event that occurred on the last day of thesensor data may be the latest available data from the sensor, or a timeat which a new meal marker has been identified/provided. The start ofthe time window for a repeating event that occurred on previous days maybe determined by meal markers from the appropriate prior days of thesame type (e.g., breakfast matched with breakfast, or generic mealmatched to the nearest time-of-day as provided by the analyte monitoringsystem's clock). Thus, using the smoothed, overlaid sensor data, thecomputer system may be programmed to identify two or more correspondingtime series of sensor data that represent excursions resulting from thesame repeating event occurring on different days.

Assuming the computer system identifies N pre-smoothed time series,there will be N−1 time series to compare against the x1 time series.Because patients' analyte levels do not follow an identical pattern dayto day, and since each breakfast, lunch, or dinner may last longer orshorter than prior days' breakfast, lunch, or dinner, it may be helpfulto perform some time dilation (or time stretching) functions on thesensor data to facilitate comparison between two different time series.Therefore, for each of the N−1 comparisons (e.g., x1 vs. x2; x1 vs. x3,. . . x1 vs. xN−1), the computer system may perform the following steps.

-   -   a. Set y1 equal to the x1 time series and define y2 to be the        next time series to compare against (e.g., x2, x3, . . . , or        xN−1).    -   b. Define a finite set of time dilation (T_(d)) parameters.    -    For example: T_(d)={5/10, 6/10, 7/10, 8/10, 9/10, 1, 10/9,        10/8, 10/7, 10/6, 10/5}    -    Where a T_(d) range of a factor of two is selected as        sufficient to cover the anticipated time span of variation        between different excursions. Other values may be used.    -   c. For each T_(d) value:        -   i. Stretch or compress y2 by the T_(d) value.        -    For example, for T_(d)=5/10=1/2, the time series y2 is            compressed to be half as short as the original. For            T_(d)=10/5=2, the time series y2 is stretched to be twice as            long as the original.        -   ii. Compute y2′, where the values are interpolated such that            the time series y2′ has the same sample time as y1. A linear            interpolation method or any other practicable method of            interpolation may be used.        -   iii. Compute the cross correlation between y1 and y2′.        -   iv. Repeat by using the next T_(d) value in the set.    -   d. Find the T_(d) value that results in the highest        cross-correlation between y1 and y2′. The T_(d) value that        results in the highest cross-correlation between y1 and y2′ is        the best time dilation adjustment.    -   e. Pairing from the start of y1 and y2′, and the subsequent        samples, compute values at each time sample k:        r1(k)=y1(k)/y2′(k),        b1(k)=median({r1(k−T _(w)), . . . , r1(k+T _(w))})        n1(k)=r1(k)/b1(k)    -    where T_(w) represents a time window large enough to enable the        calculation of the patient's baseline glucose levels relative to        each time instance k; and where n represents a        baseline-normalized ratio between two sensor glucose values.    -   f. Any time period within the x1 time series with an n1 value        below a predetermined threshold, n_low, may be identified as a        dropout. The threshold n_low may be equal to a fixed amount,        such as 0.95 for example, or in some embodiments, a variable        amount based on an initial value (e.g., 0.95) that increases as        a function of the latest sensor noise quality. Other values may        be used.    -   g. Repeat the process by replacing y2=x2 with the next time        series (e.g., set y2 equal to x3, x4, . . . , xN−1).

In another embodiment, curve smoothing, dynamic range matching, and timedilation are used to enable comparison of corresponding pre-smoothedtime series. The addition of dynamic range matching scales the analyteconcentration amplitudes to make comparison easier. For each of the N−1comparisons (e.g., x1 vs. x2; x1 vs. x3; . . . x1 vs. xN−1), thecomputer system may perform the following steps.

-   -   a. Set y1 equal to the x1 time series and define y2 to be the        next time series to compare against (e.g., x2, x3, . . . , or        xN−1).    -   b. Determine the dynamic range of y1 time series by estimating        upper (y1u), lower (y11), and average (y1a) values.    -    For example: sort all y1 values, and determine the 5^(th) and        95^(th) percentile values. Set y1u to the 95^(th) percentile        value in y1, and set y11 to the 5^(th) percentile value in y1.        Set y1a to the 50^(th) percentile value in y1. Other values may        be used.    -   c. Calculate the dynamic range of y2 time series using the same        method.    -   d. Adjust the dynamic range of y2 time series to match that of        y1.    -    For example, compute y2″:=y1a+[[y2-y2a] [y1u-y11]/[y2u-y21]]    -   e. Define a finite set of time dilation (T_(d)) parameters.    -    For example: T_(d)={5/10, 6/10, 7/10, 8/10, 9/10, 1, 10/9,        10/8, 10/7, 10/6, 10/5}    -   f. For each T_(d) value:        -   i. Stretch or compress y2″ by the T_(d) value.        -    For example, for T_(d)=5/10=1/2, the time series y2″ is            compressed to be half as short as its original. For            T_(d)=10/5=2, the time series y2″ is stretched to be twice            as long as its original. Other values may be used.        -   ii. Compute y2′, where the values are interpolated such that            the time series y2′ has the same sample time as y1. A linear            interpolation method or any other practicable method of            interpolation may be used.        -   iii. Compute the cross correlation between y1 and y2′.        -   iv. Repeat by using the next T_(d) value in the set.    -   g. Find the T_(d) value that results in the highest        cross-correlation between y1 and y2′. The T_(d) value that        results in the highest cross-correlation between y1 and y2′ is        the best time dilation adjustment.    -   h. Pairing from the start of y1 and y2′, and the subsequent        samples, compute values at each time sample        k: r1(k)=y1(k)/y2′(k),        b1(k)=median({r1(k−T _(w)), . . . , r1(k+T _(w))})        n1(k)=r1(k)/b1(k)    -   i. Any time period within the x1 time series with an n1 value        below a predetermined threshold, n_low, may be identified as a        dropout. The threshold n_low may be equal to a fixed amount,        such as 0.95 for example, or in some embodiments, a variable        amount based on an initial value (e.g., 0.95) that increases as        a function of the latest sensor noise quality. Other values may        be used.    -   j. Repeat the process by replacing y2=x2 with the next time        series (e.g., set y2 equal to x3, x4, . . . , xN−1).

In some other embodiments, the process is similar to the two embodimentsdescribed above until the point in which a dropout is identified.Instead of identifying a dropout segment whenever any one of thecomparator time series (x2, x3, . . . , xN−1) results in an n1 valuebelow a predetermined threshold, a dropout segment is identifiedwhenever any one of the comparator time series results in an n1 valuebelow a predetermined threshold n_low and no other n1 at the same timeperiod is higher than n_high where, for example, n_high equalsapproximately 1.2. Other values may be used.

The above processes compare a most recent time series against allavailable corresponding time series to identify periods within the mostrecent time series that are dropouts. FIGS. 3A to 3D and 4A to 4Cgraphically depict simplified embodiments of this process. FIG. 3Adepicts a graph 300A of a simple case where only two time series 302,304 are to be compared to identify dropouts. The plots of the two timeseries 302, 304 have been smoothed as described above. Comparing theplots 302, 304 in FIG. 3A relative to each other, reveals that theanalyte concentration data excursion caused by the event (e.g., a meal)associated with the plot 302 was larger than the analyte concentrationdata excursion caused by the event associated with the plot 304 (e.g.,more carbohydrates were consumed during the meal associated with plot302 than during the meal associated with plot 304). Thus, a dynamicrange scaling operation as described above may be used to stretch plot304 vertically to facilitate identification of any dropouts. An exampleof the results of the dynamic range scaling operation on the plot 304 isdepicted in FIG. 3B.

Analysis of FIG. 3B, which is a graph 300B, reveals that the repeatingevent associated with the plot 304 took less time than the eventassociated with the plot 302. Thus, a time dilation operation asdescribed above may be used to stretch plot 304 laterally to facilitateidentification of any dropouts. FIG. 3C is a graph 300C that illustratesan example of the results of the time dilation operation on the plot304.

FIG. 3D is a graph 300D that represents the results of dynamic rangescaling and time dilating of plot 304 so that it best matches (e.g., hasthe highest cross-correlation with) plot 302. In other words, plot 304″represents the dynamic range scaled and time dilated version of plot 304that most closely correlates (e.g., overlaps) with plot 302. With thetwo plots 302, 304 smoothed, plot 304 dynamic range scaled to plot 304′and time dilated to plot 304″, the period labeled 306 can easily beidentified as a dropout in plot 302. In some embodiments, the computersystem may provide the user with a display similar to the graph 300D ofFIG. 3D with an indication that the time period 306 represents adropout.

FIG. 4A depicts a graph 400A of another simple case where only two timeseries 402, 404 are to be compared to identify dropouts. The plots ofthe two time series 402, 404 have been smoothed as described above.Comparing the plots 402, 404 in FIG. 4A relative to each other, revealsthat the repeating event associated with the plot 404 took less timethan the event associated with the plot 402. Thus, a time dilationoperation as described above may be used to stretch plot 404 laterallyto facilitate identification of any dropouts. FIG. 4B illustrates anexample of the results of the time dilation operation on the plot 404.

From the graph 400B in FIG. 4B, a dropout can be identified as describedabove, without having to perform dynamic range scaling operation. Thus,FIG. 4C is a graph 400C that represents the results of time dilatingplot 404 so that it best matches (e.g., has the highestcross-correlation with) plot 402. In other words, plot 404′ representsthe time dilated version of plot 404 that most closely correlates (e.g.,overlaps) with plot 402. With the two plots 402, 404 smoothed and plot404 time dilated to plot 404′, the period labeled 406 can easily beidentified as dropout in plot 402. In some embodiments, the computersystem may provide the user with a display similar to the graph 400C ofFIG. 4C with an indication that the time period 406 represents adropout.

Turning now to FIG. 5, a flow chart illustrating an example method 500of the present invention is provided. In some embodiments, a signalrepresentative of sensor data is received by the computer system from ananalyte monitoring system, for example, a CGM (502). The sensor data mayinclude a historical record of an analyte level of a patient measuredover time. The sensor data is stored in a storage device of the computersystem (504). In some embodiments, the sensor data is segmented into aplurality of time series (506). Each time series is associated with adifferent instance of a repeating event. In some embodiments, the sensordata may be smoothed as described above, to facilitate the segmenting.In some embodiments, a meal start time may be identified for each of thetime series to facilitate identifying the start and end of the timeseries. The meal start time may be manually entered (e.g., based oninformation provided by the patient) or automatically determined usingestimating software as mentioned above.

Once one of the time series is selected to be analyzed for dropouts(508), the selected time series may be compared to at least one of theother time series (510). In some embodiments, time dilation and dynamicrange matching techniques as described above may be performed on thetime series used for comparison to aid in correlating the two timeseries and to compensate for different lengths of time and differentmagnitudes of the associated events. The computer system determineswhether the selected time series includes a portion that is more than apredefined threshold lower than a corresponding portion of the timeseries used for comparison (512). The predefined threshold may beselected as described above. If there is a portion of the selected timeseries that is lower than the comparison time series by more than thethreshold amount (e.g., a negative glucose level difference larger thanx amount), the computer system displays an indication that the selectedtime series includes a dropout. In some embodiments, the sensor datathat represents the dropout may be removed, set to a zero value, orotherwise marked as invalid data. In some embodiments, the dropoutsensor data may be replaced with interpolated data.

Various other modifications and alterations in the structure and methodof operation of the embodiments of the present disclosure will beapparent to those skilled in the art without departing from the scopeand spirit of the present disclosure. Although the present disclosurehas been described in connection with certain embodiments, it should beunderstood that the present disclosure as claimed should not be undulylimited to such embodiments. It is intended that the following claimsdefine the scope of the present disclosure and that structures andmethods within the scope of these claims and their equivalents becovered thereby.

What is claimed is:
 1. A method, comprising: receiving sensor data froman analyte sensor configured for positioning in fluid contact with afluid under a skin layer, the sensor data corresponding to a monitoredanalyte level of the fluid, the sensor data including multiple instancesof a periodic event; segmenting the sensor data into a plurality of timesegments, the plurality of time segments including a first segmentcomprising a first instance of the periodic event, and a second segmentcomprising a second instance of the periodic event; applying a timedilation operation to the first segment to obtain a time dilated firstsegment; correlating the sensor data of the time dilated first segmentto the sensor data of the second segment, so that sensor data of thetime dilated first segment over a first period of time correlates tosensor data of the second segment over a second period of time;determining that the sensor data of the time dilated first segment overthe first period of time differs by more than a dynamically varyingthreshold from the sensor data of the second segment over the secondperiod of time; and displaying an indication that the first segmentincludes a dropout.
 2. The method of claim 1, wherein the periodic eventis a meal, and further comprising receiving a user input for a timemarker of at least the first instance of the periodic event or thesecond instance of the periodic event.
 3. The method of claim 1, whereinapplying the time dilation operation to the first segment comprisesdetermining a set of time dilation parameters, and applying the set oftime dilation parameters to the first segment to obtain the time dilatedfirst segment; and wherein determining that the sensor data of the timedilated first segment over the first period of time differs by more thanthe dynamically varying threshold from the sensor data of the secondsegment comprises determining a ratio for the sensor data of the timedilated first segment and the sensor data of the second segment.
 4. Themethod of claim 1, wherein the first segment is the most recent segmentof the plurality of time segments.
 5. The method of claim 1, whereindisplaying the indication that the first segment includes a dropoutcomprises displaying an indication on a display for a user.
 6. Themethod of claim 1, wherein the dynamically varying threshold variesaccording to a noise quality of the analyte sensor.
 7. The method ofclaim 1, wherein the first period of time and the second period of timeare of different lengths.
 8. The method of claim 1, wherein correlatingthe sensor data of the time dilated first segment to the sensor data ofthe second segment comprises generating a visual representation of thefirst segment and a visual representation of the second segment,dilating the visual representation of the first segment to obtain a timedilated visual representation of the first segment, and overlapping thetime dilated visual representation of the first segment with the visualrepresentation of the second segment.
 9. The method of claim 1, whereinthe sensor data from the analyte sensor is received continuously. 10.The method of claim 1, further comprising marking the sensor data of thetime dilated first segment which differs by more than the dynamicallyvarying threshold from the sensor data of the second segment over thesecond period of time as invalid.
 11. A system, comprising: an analytesensor configured for positioning in fluid contact with a fluid under askin layer to generate sensor data corresponding to a monitored analytelevel of the fluid, the sensor data including multiple instances of aperiodic event; and a receiving device comprising a display, one or moreprocessors, and a memory storing instructions which, when executed bythe one or more processors, cause the one or more processors to: receivesensor data from the analyte sensor; segment the sensor data into aplurality of time segments, the plurality of time segments including afirst segment comprising a first instance of the periodic event, and asecond segment comprising a second instance of the periodic event; applya time dilation operation to the first segment to obtain a time dilatedfirst segment; correlate the sensor data of the time dilated firstsegment to the sensor data of the second segment, so that sensor data ofthe time dilated first segment over a first period of time correlates tosensor data of the second segment over a second period of time;determine that the sensor data of the time dilated first segment overthe first period of time differs by more than a dynamically varyingthreshold from the sensor data of the second segment over the secondperiod of time; and display an indication that the first segmentincludes a dropout.
 12. The system of claim 11, wherein the periodicevent is a meal, and wherein the memory further stores instructionswhich, when executed by the one or more processors, cause the one ormore processors to receive a user input for a time marker of at leastthe first instance of the periodic event or the second instance of theperiodic event.
 13. The system of claim 11, wherein the one or moreprocessors apply the time dilation operation to the first segment by atleast determining a set of time dilation parameters, and applying theset of time dilation parameters to the first segment to obtain the timedilated first segment; and wherein the one or more processors determinethat the sensor data of the time dilated first segment over the firstperiod of time differs by more than the dynamically varying thresholdfrom the sensor data of the second segment by at least determining aratio for the sensor data of the time dilated first segment and thesensor data of the second segment.
 14. The system of claim 11, whereinthe first segment is the most recent segment of the plurality of timesegments.
 15. The system of claim 11, wherein the dynamically varyingthreshold varies according to a noise quality of the analyte sensor. 16.The system of claim 11, wherein the first period of time and the secondperiod of time are of different lengths.
 17. The system of claim 11,wherein the one or more processors correlate the sensor data of the timedilated first segment to the sensor data of the second segment by atleast generating a visual representation of the first segment and avisual representation of the second segment, dilating the visualrepresentation of the first segment to obtain a time dilated visualrepresentation of the first segment, and overlapping the time dilatedvisual representation of the first segment with the visualrepresentation of the second segment.
 18. The system of claim 11,wherein the one or more processors receive the sensor data from theanalyte sensor continuously.
 19. The system of claim 11, wherein thememory further stores instructions which, when executed by the one ormore processors, cause the one or more processors to mark the sensordata of the time dilated first segment which differs by more than thedynamically varying threshold from the sensor data of the second segmentover the second period of time as invalid.
 20. The system of claim 11,wherein the memory further stores instructions which, when executed bythe one or more processors, cause the one or more processors to replacethe sensor data of the time dilated first segment which differs by morethan the dynamically varying threshold from the sensor data of thesecond segment over the second period of time with a predeterminedvalue.